The invention relates to a device for automatic diagnosis of a technical system.
The state of the technology is described below.
The object of the technical diagnosis is to localize the component errors that occur in a specific technical system (tS). For this purpose, values of parameters must be measured at tS.
The state of the technology encompasses different methods and devices for diagnosing a technical system tS through the evaluation of a model of tS. It is known that it is often costly to construct a model that is adequately realistic. To reduce the expenses when a plurality of similar components is installed in the technical system, or when a plurality of similar technical systems is to be modeled and subsequently diagnosed, approaches have been developed for describing the object to be diagnosed with a component-oriented model. The model reflects the structure of the technical system in components. The generic scope of the invention falls into this category. In contrast, numerous modeling approaches from the regulating technique, such as differential equations, are not component-oriented.
Prior to the diagnosis, a human expert must input once the information about the structure and function of the technical system tS, and the possible errors and the determination of these errors, that the diagnosis device requires for searching the errors. This process, which is often referred to as knowledge acquisition, is time consuming and error-prone. Therefore, a principal challenge is to design the diagnosis device to operate with as little input information as possible and automatically generate as much of the required information as possible from the input information.
The attached drawings illustrate the function of a model-based diagnosis device by way of an example.
The foundation of the present claim to patentability is described in the overview article by Randall Davis and Walter Hamscher: xe2x80x9cModel-Based Reasoning; Troubleshootingxe2x80x9d in: Walter Hamscher and Luca Console and Randall Davis: xe2x80x9cReadings in Model-Based Diagnosis,xe2x80x9d Morgan Kaufmann Publ. (1992), pp. 1-24. Each component of tS possesses different behavioral modes; in the simplest case, the modes are xe2x80x9cintactxe2x80x9d and xe2x80x9cdefective.xe2x80x9d A diagnosis allocates each component a behavioral mode, thereby specifying which components are defective.
The term xe2x80x9cdiagnosisxe2x80x9d has two meanings: First, it refers to the process of finding the errors in the technical system tS and, second, it describes the result of this search.
An important class of model-based diagnosis methods is presented in the above-cited article by Randall Davis and Walter Hamscher, namely the General Diagnostic Engine (GDE). The aforementioned figures illustrate the function of such a GDE by way of a simple example.
In FIG. 1, C1 and C2 double their respective input value. C3 forms the difference between its two input values, and C4 increases its input value by 5. The values 2 and 5 were supplied to this circuit, and 8 and 20 were measured as output values. Under the assumption that all of the components operate error-free, 6 or 15 would have to be the attained result. Consequently, a component is defective.
If a measurement has revealed that the value x is present at the input of the component C2, and if f2 and f4, the behavioral models for the components C2 and C4, respectively, are in the xe2x80x9cintactxe2x80x9d behavioral mode, the model predicts the value z=f4(f2(x)) for the output of C4 if it is assumed that C2 and C4 are intact. This is because y=f2(x) in this example is simultaneously the output of C2 and the input of C4.
For each model value (every value that is predicted by the model), FIG. 2 notes which assumptions must be met so that the model yields this value. For example, the value 15 is obtained under the assumption that both C2 and C4 are intact. Two desired/actual differences were determined; therefore, there are two conflict sets. On the one hand, C1, C2 and C3 cannot be intact simultaneously because of the deviation at Output_1; on the other hand, C2 and C4 cannot simultaneously be intact because of Input_2.
The candidate set prior to the beginning of the diagnosis includes only one candidate, namely the one that allocates all components the behavioral mode xe2x80x9cintact.xe2x80x9d The candidate set is expanded with the aid of the conflict set to subsequently reduce the candidate set to its minimum through assumptions and measures.
In the first step, the conflict set is evaluated as follows:
{(C1, intact), (C2, intact, (C4, intact)},
and in the second step, the conflict set is evaluated as
{(C2, intact), (C4, intact)}.
The candidate set after the first step with the first conflict set includes the candidates [(C1, defective), (C2, intact), (C3, intact), (C4, intact)].
This candidate set is refuted by the second conflict set. The corrected, direct successors are [(C1, defective), (C2, defective), (C3, intact), (C4, intact)] and [(C1, defective), (C2, intact), (C3, intact), (C4, defective)]. The two successors are consistent with the two observations, and are therefore incorporated into the candidate set after Step 2.
The following options are available for continuing the diagnosis in this situation and reducing the candidate set:
The first option determines further measured values. In the event that the desired value 4 is present at the output of C1, GDE supplies {(C1, defective)} as the third conflict set, and excludes all candidates that allocate C1 the defective mode.
A further option involves a xe2x80x9csingle fault assumption,xe2x80x9d i.e., it is assumed that, at the most, one component is defective at any one time. The diagnosis of [(C1, intact), (C2, defective), (C3, intact), (C4, intact)] follows immediately afterward.
The physical behavior of each component that appears at least once in tS is described one time in the definition of the input and output parameters and the internal parameters of the component, and in the description of the connections between the parameters as relations (constraints). A special type of component parameter is the behavioral modes of the component. The constraints of a component can be universal, and be allocated to certain behavioral modes of the component, which means that they are only applicable when the component has assumed the respective behavioral mode.
Model-based diagnosis presupposed that the behavioral of the components can be described locally, that is, the allocation of values to each component""s parameters is solely dependent on the allocation of values to other parameters of the same component. Then, it is only necessary to describe each type of component once; the diagnosis device can re-use this description, which significantly reduces the amount of required input information.
The technique of model-based diagnosis is especially advantageous in comparison to other methods if the components to be described are of a simple nature (e.g., electrical or hydraulic components) and appear numerous times in tS. The diagnosis device thus evaluates a library with the description of all component types, as well as a construction model of the technical system. This construction model describes how the components are connected to one another. A component Comp_1 is connected to a component Comp_2 through the identification of the parameter Para_1 at Comp_1 with a parameter Para_2 at Comp_2. The construction model further describes the type of each component. The diagnosis device automatically constructs the model of the technical system tS from the descriptions of the component types and the construction model.
Most of the known diagnosis methods, and the diagnosis device of the invention, presuppose that the technical description of the system tS to be diagnosed does not change during a diagnosis, in other words, the library with models of component types and the construction model are valid for the entire diagnosis. The set of diagnoses that are possible in theory is therefore set at the start of the diagnosis: Each allocation of behavioral modes to all components of the technical system is a possible diagnosis. The diagnosis device iteratively limits the extremely-large set of possible diagnoses during the diagnosis process. The most important type of limitation is that parameters are allocated measured or observed values. During the diagnosis, each model-based method repeatedly determines which diagnoses are still possible in view of the parameter values that have been determined to this point. For this purpose, the method allocates behavioral modes piecewise to a few components, and determines which values certain parameters of the technical system would have to assume in this allocation, which is often called a hypothesis. If this prediction does not match the measurements, the hypothesis is refuted and retracted. A diagnosis device must store information about the measures that can be implemented over the course of the diagnosis. Measures are necessary for determining parameter values or bringing the technical system into a certain state. A measure can be linked to prerequisites, that is, the measure may only be performed if the prerequisite is satisfied. The prerequisite can stipulate that certain parameters have assumed certain values. The measures that can be performed during a diagnosis are associated with different costs; this must be taken into account in the selection of the respectively next measure. These requirements are explained by way of two examples from practice (diagnosis of motor-vehicle electronics):
Some measures can or may only be implemented in certain situations during a diagnosis, and not in others. An example: The ignition time can only be determined when the engine is running. The measurement xe2x80x9cignition timexe2x80x9d is therefore associated with the prerequisite xe2x80x9cengine is running.xe2x80x9d
The two following measures incur widely-varying implementation costs:
the inquiry of an error code that is stored in a control device; and
the measurement that indicates the internal resistance of a certain electrical line, the measurement requiring the removal of particular components.
In many studies, measures with which parameter values are observed differ from measures through which parameters obtain specific values. The first type includes the query: xe2x80x9cIs the engine running?xe2x80x9d, while the second one includes the measure xe2x80x9cTurn on engine.xe2x80x9d
Diagnosis devices of the state of the technology operate with methods of automatically determining the respectively next measure. Two approaches are described in Johan de Kleer and Brian Williams: xe2x80x9cDiagnosing Multiple Faults,xe2x80x9d in: Walter Hamscher and Luca Console and Randall Davis: xe2x80x9cReadings in Model-Based Diagnosis,xe2x80x9d Morgan Kaufmann Publ. (1992). The approaches utilize probability-theory methods to evaluate the anticipated effectiveness of any possible measure as entropy. Their method is known in the literature as the xe2x80x9csystematic observation proposal.xe2x80x9d Because the precise calculation of the de posteriori probabilities and thus of the entropies after the input of an additional known value is very calculation-intensive, the authors propose to replace the de posteriori probabilities of diagnoses that continue to be possible with the a priori probabilities, and replace the de posteriori probabilities that are valid prior to the measurement, but no longer after the measurement, with zero. The authors propose further simplifications as well.
The number of measurements taken over the course of a diagnosis, and the speed of a diagnosis, are extensively dependent on probabilities. There is no systematic, engineer-style procedure for structuring such probabilities for arbitrary partial systems. Probabilities can be estimated for a technical system if numerous errors have already occurred in the system and measurements were taken, so data material is present for statistical analyses.
In Peter Struss: xe2x80x9cTesting for Discrimination of Diagnosis,xe2x80x9d Proceed. 5th Intern. Workshop on Principles of Diagnosis, New Paltz (N.Y., USA), pp. 312-320 (1994), Struss indicates that parameters can assume different values under different prerequisites, that is, with different values of other parameters. In this essay, a generalization of the systematic observation proposal is formulated; here, it is called xe2x80x9csystematic test proposal.xe2x80x9d The fundamental principle of his proposed solution lies in calculating the best prerequisites for a parameter p to be able to distinguish among a given set of diagnoses that are still possible. If these prerequisites are values for parameters that must be measured in turn, and cannot be set, their prerequisites are also calculated, etc.
A systematic method of modeling the sequence of measures is described in Ying Sun and Daniel Weld: xe2x80x9cA Framework for Model-Based Repair,xe2x80x9d Proceed. 11th National Conference on Artificial Intelligence, Monterey (USA), pp. 182-187 (1993). Constructs were defined for measures, provided with cost information and incorporated into the diagnosis algorithm. A measure is described by a parameter of the technical system tS to which it relates, a logical predicate that represents the prerequisites of the measure, and its purposes. The prerequisites can be arbitrary value contexts of the technical system. A value context is the allocation of values to parameters of the technical system. The cost of a measure can be a function of its prerequisites. This essay describes a method of estimating the cost of a repair plan from the cost for the involved measures, and a formula is given for estimating the cost of a measure in advance.
There is no indication, however, of how the costs for the individual measures are obtained and stored efficiently in reality. The only way of storing the information about measures is the obvious process of a human expert making all information about the possible measures directly available to the diagnosis device. Consequently, the expert must enter the information xe2x80x9cby handxe2x80x9d; hence, the diagnosis device requires a large amount of input information.
C. A. Catino et al. describe in xe2x80x9cAutomatic generation of quality models of chemical process units,xe2x80x9d Computers and chemicals engineering, Vol. 15, No. 8, pp. 583-599 (1991) how a model of a technical system is automatically generated from re-usable models of components that are stored in a library. The application described in C. A. Catino et al. is a chemical process, and the components are partial processes. These component models include
xe2x80x9cquality conditionsxe2x80x9d;
xe2x80x9crelationsxe2x80x9d (quantitative constraints on how model parameters can be determined from other model parameters); and
xe2x80x9cinfluencesxe2x80x9d (qualitative descriptions of how parameters influence other parameters).
xe2x80x9cOperating assumptionsxe2x80x9d are also mentioned.
The article mentions that the model can also be used for diagnosis: Compare measured values with desired values that are predicted in the evaluation of the model, and draw conclusions about errors in the technical system from differences between these values.
As is apparent, measures must be implemented for determining the measured values. C. A. Catino et al. do not, however, illustrate how the described device automatically finds these measures and tests which measures are permissible under certain operating conditions and/or measured values.
The essay by Catino et al. does not describe how to organize and store the information that establishes how to measure parameters and under which conditions these measurements can be taken. The article neglects to mention that the measures and prerequisites are elements of component models, as well as how these prerequisites are tested regarding the operational time. Hence, no procedure is given for performing the task successfully with the least input information.
In Jiah-Shing Chen et al.: xe2x80x9cAction Selection in Interactive Model-Based Diagnosis,xe2x80x9d Proc. Eighth Conf. Artificial Intelligence Applications, Monterey (USA), pp. 67-73 (1993), the authors treat the task of automatically selecting measuresxe2x80x94more precisely, xe2x80x9ctestsxe2x80x9d for measuring variables and xe2x80x9creplacement actionsxe2x80x9d for a piecewise component exchange. Every possible failure is assigned a probability, and a constraint that is derived from a probability-theory model selects the measure that, among all possible measures, is the most beneficial, as a function of the values measured to this point. This measure is the one that can be expected to have the lowest cost for the further diagnosis.
The essay by Jiah-Shing Chen et al. also does not specify how these xe2x80x9ctestsxe2x80x9d and xe2x80x9cactionsxe2x80x9d should be stored. It merely mentions the obvious alternative of defining and storing all possible measures in advance. This can be extremely costly: As an example, the technical system is a circuit having 100 resistors and 50 lighting fittings. Two xe2x80x9ctestsxe2x80x9d and one xe2x80x9creplacement actionxe2x80x9d can be executed for each circuit and each lighting fitting. Therefore, 300 xe2x80x9ctestsxe2x80x9d and 150 xe2x80x9creplacement actionsxe2x80x9d must be defined and stored. The essay by Jiah-Shing Chen et al. does not disclose an efficient means of determining the measure (i.e., the xe2x80x9ctestxe2x80x9d or xe2x80x9creplacement actionxe2x80x9d) that is to be implemented next. The paper derives a calculation constraint xcex94(M) for each measure M as a measure of the cost to be expected for the further diagnosis if the measure M is implemented as the next measure. The only disclosed procedure of the diagnosis device for determining the next measure is the determination of the value xcex94(M) for each measure M that is still possible, then the implementation of the measure M0 for which xcex94 is minimal. This procedure does not work in practice: In the above example, xcex94 must be applied to 450 measures. These 450 calculations must be repeated (after the first measure, only 449 calculation procedures remain, then 448 after the second measure, and so on) until the error has been found and remedied. Should only 20 measures be necessary for a complete diagnosis (for 450 components, this is an outstanding value), xcex94 must be applied to a measure 450+449+ . . . +431=8.790 times during a diagnosis; even a single calculation process can be complicated. The procedure disclosed in the paper is applicable for imaginary examples, but not for technical systems in the real world.
Without explicitly stating this, the paper apparently presupposes that
any measure can be implemented at any time; and
the implementation of each measure is equally costlyxe2x80x94the only difference is the number of further measures that are still necessary after a measure, and the fact that the anticipated further cost is determined from these different values.
These assumptions are not applicable in practice, for example in the diagnosis of motor-vehicle electronics. An efficient diagnosis device must therefore be constructed with this fact in mind.
In xe2x80x9cAutomaticka diagnostika poruch energetickych zarizeni cislicovym pocitacem,xe2x80x9d Energetika Vol. 30, No. 1, pp. 21-24 (1980), Vitezlav Benes presents a method of determining the measure to be implemented next.
This method evaluates occurrence probabilities for errors and the probabilities that specific errors will be found in certain tests on the object to be diagnosed. The probability theory (Bayes rules) and information theory (Shannon entropy) are used to determine which effectiveness has a test that has not yet been performed. The cost for the test is compared to this effectiveness, and the test having the most favorable cost-effectiveness ratio is selected. This paper also fails to indicate a procedure for efficiently determining the cost information from the least possible input information, and how fast the next measure is determined during the diagnosis procedure. The only apparent procedure is how the human expert makes all measures (=tests) that can be performed on the technical system directly available to the diagnosis device, and the cost-effectiveness ratio is to be calculated prior to each diagnosis step for all measures that are still possible. This procedure is feasible for the cited example, which has 20 errors and 20 possible measurements. It is inadequate for more complex examples comprising thousands of possible errors and tests. This essay also does not indicate how the diagnosis device operates with the least possible input information.
Numerous other approaches also employ an entropy function to determine the effectiveness that the knowledge about a parameter value would have. A cited example is the essay by Johan de Kleer and Brian Williams: xe2x80x9cDiagnosing Multiple Faults,xe2x80x9d in: Walter Hamscher and Luca Console and Randall Davis: xe2x80x9cReadings in Model-Based Diagnosis,xe2x80x9d Morgan Kaufmann Publ. (1992).
The work by Vitezlav Benes also does not indicate how to structure diagnosis devices for a plurality of technical systems other than to start xe2x80x9cfrom zeroxe2x80x9d again each time. A single application example, namely the oil-filter system in a 100-kV transformer, is presented. All of the probabilities must be re-entered for a different application example.
Practically all diagnosis methods known from the state of the technology, and a diagnosis device within the generic scope of the present inventions, operate with an inference machine. An inference machine repeatedly reaches (xe2x80x9cinfersxe2x80x9d or xe2x80x9cpropagatesxe2x80x9d) conclusions about values of further parameters from parameter values that have already been calculated or measured, based on the model of tS. Mechanisms have been developed for re-using existing calculation results instead of performing re-calculations. Of course, calculation results can only be re-used if the context that was presupposed in the calculations is still valid. These mechanisms store and xe2x80x9cadministerxe2x80x9d parameter values of value contexts, namely the allocations of values to parameters. A mechanism of this type is referred to as the xe2x80x9cReason Maintenance Systemxe2x80x9d (RMS) or the xe2x80x9cTruth Maintenance Systemxe2x80x9d (TMS). Before the inference machine derives parameter values, it xe2x80x9cconsultsxe2x80x9d the RMS regarding known parameter values. In the cited article by Randall Davis and Walter Hamscher: xe2x80x9cModel-Based Reasoning; Troubleshootingxe2x80x9d in: Walter Hamscher and Luca Console and Randall Davis: xe2x80x9cReadings in Model-Based Diagnosis,xe2x80x9d Morgan Kaufmann Publ. (1992), the use of a special RMS, the xe2x80x9cAssumption-Based Truth Maintenance System,xe2x80x9d is described for model-based diagnosis as xe2x80x9cenabling technologyxe2x80x9d for the xe2x80x9cGeneral Diagnostic Enginexe2x80x9d (GDE), which is a class of diagnosis devices. In xe2x80x9cBuilding Problem Solvers,xe2x80x9d MIT Press (1993), Ken Forbus and Johan de Kleer describe different types of RMSes. FIG. 3 shows an RMS that is configured as an ATMS, in the form of the circuit according to FIG. 1.
An ATMS is formally a directional graph having two types of nodes:
An ATMS node stands for an assumption or a statement about the technical system. FIG. 3 shows a corresponding ATMS graph.
A xe2x80x9cjustificationxe2x80x9d asserts why a statement was derived. In connection with model-based diagnosis, a justification stands for a relation among the parameters of a component, which were derived from a behavioral model with constraints.
An ATMS node performs one of the following three tasks:
1. Allocation of a behavioral model of a component to a node for an assumption;
2. allocation of a pair (parameter, value) to a node for an observation; or
3. allocation of a triplet (parameter, value, marking) to a node for a derived statement (prediction). A marking (label) is a single environment or an OR link of environments. An environment lists the assumptions under which the parameter value is valid. In the context of model-based diagnosis, an environment is a list of the behavioral modes of components that must be present so that the parameter can be predicted based on the observations with the overall model. A specially characterized ATMS node for a derived statement is the conflict-set node (contradiction node), at which the problem solver can read off the conflict sets. Its marking is determined exactly like the marking of every other ATMS node for derived statements. Each environment is a conflict set in its marking.
The justifications, assumptions, observations and statements originate from the problem solver. The object of the ATMS is to efficiently derive the assumptions, among which one statement is applicable, and to update them after these new observations.
As requested by the problem solver (inference machine), the ATMS designates a node for each observed value and each calculated value of a parameter, and for each behavioral mode. The problem solver controls the ATMS through the following xe2x80x9cmessagesxe2x80x9d:
1. It xe2x80x9ctransmitsxe2x80x9d the measured parameter values and behavioral modes to the ATMS; and
2. It calculates parameter values from known values. The ATMS notes these derivations as justifications. The problem solver applies the marginal conditions and the behavioral models, not the ATMS itself. The problem solver, not the ATMS, evaluates the fact that, if the component C1 is intact, its initial value is two times the input value.
In FIG. 3, the problem solver has discovered two discrepancies, and the ATMS has noted them as two justifications, and determined the current conflict sets as environments of the contradiction node.
If no contradictions were observed, the output value of C3 would actually be 6, while the initial value of C4 would be 15, so no new ATMS node would be generated; rather, the nodes for the statements Output_C3=6 and Output_C4=15 would be updated. This can be attributed to the fact that values have been observed that cause the nodes to become nodes for observations, that is, the markings are canceled. Also, all of its successors are updated.
The ATMS automatically administers the marking, that is, the set of environments, of each node. The original ATMS notes the current marking such that
1. the statement about the node is applicable for each environment (soundness);
2. no environment is explicitly refuted (consistency);
3. each environment in which the statement about the node is applicable is noted at the node, or is a superset of a noted environment (completeness); and
4. no noted environment is a partial set of another environment (minimality).
The original GDE, used in conjunction with an original ATMS, determines all conflict sets after each observation, and then all diagnosis candidates. The original ATMS therefore re-calculates all markings after each observation, so each marking of a node satisfies the four aforementioned criteria of soundness, consistency, completeness and minimality following each calculation step. This method requires a calculation time that increases exponentially in the number of statements, and is therefore very calculation-intensive.
In xe2x80x9cDiagnosing systems modeled with piecewise linear constraints,xe2x80x9d Proceed. TAI90xe2x80x94Tools for Artificial Intelligence, Herndon, Nov. 6-9 (1990), Henri Beringer and Bruno de Backer describe a method of converting the model in advance such that the work of the inference machine takes little time. Because this conversion is performed in advance, it is not dependent on parameter values obtained during the diagnosis. Therefore, no proposal is given for quickly deriving further values from measured values and hypotheses about behavioral modes.
The state of the technology includes a class of methods and devices for limiting the operational-time requirement of the RMS, namely the focusing RMS. A xe2x80x9cfocusxe2x80x9d is a memory that functions as an interface between the inference machine and the RMS. The inference machine writes value contexts, that is, value allocations of parameters, into the focus. This is often the current most plausible diagnosis of the diagnoses that are still possible, that is, certain behavioral modes of components; diagnoses that were plausible before can be refuted through further measurements. The RMS only completely administers the dependencies for the value contexts in the focus. Whenever the inference machine changes the focus, the RMS again determines dependencies. Different types of focusing RMSes are presented in the book by Ken Forbus and Johan de Kleer, specifically the JTMS (pp. 171-194) and the LTMS (pp. 265-305). An especially efficient embodiment of a focusing RMS is the two-view ATMS known from Mugur Tatar: xe2x80x9cCombining the Lazy label Evaluation with Focusing Techniques in an ATMS,xe2x80x9d Proceed. 11th European Conference on Artificial Intelligence, Amsterdam (1994).
The article by C. A. Catino et al. also mentions conditions and focuses. Unlike in the invention, however, they do not decide on the validity of measures. Instead, these conditions limit the model such that the model can only be observed for specific operating conditions (namely the ones to be investigated). Three focusing techniques are named for this purpose. There is no mention of who selects the information that is stored in the focus. Obviously, a human user selects the information once and, during the model generation, the focus remains unchanged. Therefore, the focus cannot be dependent on measured results obtained over the course of the diagnosis. The focus cannot, in particular, include the diagnoses that are the most probable or most plausible based on the previous measurements.
Inference results can be dependent on two types of influence variables:
Which values are allocated to freely-selectable input parameters of the technical system; and
in which behavioral modes the components of the technical system are located.
The state of the technology encompasses numerous methods in which the inference machine derives further parameter values, and possible diagnoses, from the measured values for a single value allocation of input parameters. Clearly, however, operational time is saved when the inference results that are valid in the old and the new value allocations are re-used for a different value allocation to input parameters. This procedure is first treated in Oskar Dressler and Hartmut Freitag, xe2x80x9cPrediction Sharing Across Time and Contexts,xe2x80x9d Proceed. 12th National Conference on Artificial Intelligence, Seattle (USA), pp. 1136-1141 (1994), and formulated such that inference results are re-used for different times. This paper presents how assumptions are modeled for temporal aspects. There is, however, no proposal for configuring the interface between the RMS and the inference machine, and how this interface is controlled. There is also no indication of how calculations are made for only one time, on the one hand, yet interference results can be re-used for different times.
The object of the invention is described below.
The object of the present invention is to provide a model-based device and a corresponding method, which is based on this device, for diagnosing a technical system comprising components,
the device utilizing as little input information as possible to automatically generate all of the required information about measures that are implemented over the course of the diagnosis, and
the device diagnosing the technical system quickly and at the lowest cost, with these measures being implemented and evaluated.
Specifically, this means that:
In a xe2x80x9cmodel-based device,xe2x80x9d the diagnosis device uses a component-oriented model of the object to be diagnosed, which is automatically generated from a library of component models and a construction model of the object to be diagnosed (generic scope of the invention).
Measure-related information includes the prerequisites under which the measure may be implemented, and the cost of implementing the measure.
The requirement of the least possible input information means that the information about measures is to be stored in the device such that duplicate information need only be stored once, and can be re-used automatically. This applies for measures that determine parameter values, as well as for those that set parameters at specific values.
The requirement for a fast diagnosis particularly means that the diagnosis device should only perform inferences for a given allocation of values to input parameters and an allocation of behavioral modes at components, yet re-use the results of these inferences for other value allocations or other allocations of behavioral modes. The diagnosis device should include an RMS for noting on which value allocations and/or allocations the inference results depend.
The concept of the invention is to formalize and store the required information about measures in a particular manner, namely to define them locally for the component types. Therefore, not only are parameters and constraints stored in the library, but information about the measures relating to the parameters of the component type are also stored there. The establishment of the parameters to which a measure relates, and an identification of the cost, are stored locally.
The diagnosis device evaluates the component-oriented system model to determine the validity of a measure in a predetermined value context. It does this through the propagation of parameter values through the model by the inference machine until the diagnosis device can decide whether the measure is valid in the value context.
So that the diagnosis device operates as quickly as possible and only makes the respectively necessary calculations, the focus was embodied as an interface between the inference machine and the RMS in an inventive manner.
The invention is described below.